Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/1051

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DC Field

Value

Language

dc.contributor.author

Samantaray, S R

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dc.contributor.author

Dash, P K

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dc.date.accessioned

2009-10-06T08:22:45Z

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dc.date.available

2009-10-06T08:22:45Z

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dc.date.issued

2009

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dc.identifier.citation

European Transactions on Electrical Power (2009)

en

dc.identifier.uri

http://dx.doi.org/10.1002/etep.321

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dc.identifier.uri

http://hdl.handle.net/2080/1051

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dc.description.abstract

The paper presents an intelligent technique for high impedance fault (HIF) detection using combined extended kalman filter (EKF) and support
vector machine (SVM). The proposed approach uses magnitude and phase change of fundamental, 3rd, 5th, 7th, 11th and 13th harmonic component as feature inputs to the SVM. The Gaussian kernel based SVM is trained with input sets each consists of ‘12’ features with corresponding target vector ‘1’ for HIF detection and ‘1’ for non-HIF condition. The magnitude and phase change are estimated using EKF. The proposed approach is trained with 300 data sets and tested for 200 data sets including wide variations in operating conditions and provides excellent results in noisy environment. Thus, the proposed method is found to be fast, accurate, and robust for HIF detection in distribution feeders.

en

dc.format.extent

202829 bytes

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dc.format.mimetype

application/pdf

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dc.language.iso

en

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dc.publisher

Wiley Interscience

en

dc.subject

High impedance fault detection

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dc.subject

Support vector machine

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dc.subject

Distribution feeder

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dc.subject

Extended kalman filter

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dc.title

High impedance fault detection in distribution Feeders using Extended Kalman Filter and Support Vector Machine